The representation of planning strategies

AbstractAn analysis of strategies, recognizable abstract patterns of planned behavior, highlights the difference between the assumptions that people make about their own planning processes and the representational commitments made in current automated planning systems. This article describes a project to collect and represent strategies on a large scale to identify the representational components of our commonsense understanding of intentional action. Three hundred and seventy-two strategies were collected from ten different planning domains. Each was represented in a pre-formal manner designed to reveal the assumptions that these strategies make concerning the human planning process. The contents of these representations, consisting of nearly one thousand unique concepts, were then collected and organized into forty-eight groups that outline the representational requirements of strategic planning systems

Learning Classical Planning Strategies with Policy Gradient

A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributions of planning problems and a selected performance metric, e.g. the IPC score. We instantiate the framework by constructing a policy space consisting of five search approaches and a two-dimensional representation of the planner's state. Then, we train the system on randomly generated problems from five IPC domains using three different performance metrics. Our experimental results show that the learner is able to discover domain-specific search strategies, improving the planner's performance relative to the baselines of plain best-first search and a uniform policy.Comment: Accepted for ICAPS 201

Combined Reinforcement Learning via Abstract Representations

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.Comment: Accepted to the Thirty-Third AAAI Conference On Artificial Intelligence, 201

Citizen Participation and Strategic Planning for an Urban Enterprise Community

Public policies rarely have single objectives. For the federal Empowerment Zones and Enterprise Communities initiative, bettering the socioeconomic opportunity structure among a collection of the nation\u27s low-income areas is only one of its goals. Another initiative objective is to foster the representation of common citizens, especially residents, in the planning and implementation of strategies and programs designed to redevelop these low-income areas. Strategic community planning was the method chosen by the initiative\u27s designers to achieve both objectives. This article, which makes use of the case study approach, addresses strategic community planning as an instrument of advancing citizen representation in urban redevelopment processes. Specifically, it describes and critiques the process jointly administered in three upstate New York cities — Albany, Schenectady, and Troy — that are participating in the urban portion of the federal initiative. The purpose of this study is to assess the degree to which residents of the low-income areas of these three cities participated in the strategic community planning process

Learning Strategies To Develop Vocabulary Used By An Eight Grader Of RSBI Of SMPN 2 Karanganyar

This research is about learning strategies used by an eighth grader of RSBI program at SMPN 2 Karanganyar to develop vocabulary. The objective of this research is to describe learning strategies used by an eighth grader of SMPN 2 Karanganyar to develop vocabulary that is divided into four aspects, namely 1) learning strategies to understand word meaning, 2) learning strategies to develop pronunciation skill, 3) learning strategies to develop spelling skill, and 4) learning strategies to understand grammar. The setting of this research is SMPN 2 Karanganyar. Then, the type of this research is case study. The subject of this research is an eighth grader at SMPN 2 Karanganyar. The writer uses interview and observation to collect the data. The data analysis technique is from Miles and Huberman theory. The results of this research are separated into four, as follows: 1) The learning strategies used to understand word meaning are 6 meta-cognitive strategies, 11 cognitive strategies, and 2 socio-affective strategies. The metacognitive strategies can be divided into advance organizer, directed attention, self management, functional planning, delayed production, and self evaluation. And the cognitive strategies are repetition, resourcing, translation, grouping, note taking, deducation, imagery, auditory representation, key word, transfer, and inferencing. Then, the socio-affective strategies are cooperation and question for clarification. 2) The learning strategies to develop pronunciation skill are 6 meta-cognitive strategies, 7 cognitive strategies, and 2 socio-affective strategies. The meta-cognitive strategies are advance organizer, directed attention, self management, self monitoring, delayed production, and self evaluation. And the cognitive strategies are repetition, resourcing, grouping, deducation, imagery, auditory representation, and inferencing. Then, the socio-affective strategies are cooperation and question for clarification.3) The learning strategies to develop spelling skill are 4 meta-cognitive strategies, 5 cognitive strategies, and 2 socio-affective strategy. The meta-cognitive strategies are directed attention, self management, delayed production, and self evaluation. And the cognitive strategies are repetition, resourcing, translation, auditory representation, and key word. Then, the socio-affective strategies are cooperation and question for clarification.4) The learning strategies to understand grammar are 5 meta-cognitive strategies, 6 cognitive strategies, and 1 socio-affective strategy. The meta-cognitive strategies are directed attention, self management, functional planning, delayed production, and self evaluation. And the cognitive strategies are resourcing, translation, grouping, note taking, transfer, and inferencing. Then, the socio-affective strategy is question for clarification. The learning strategies that she used to understand word meaning are the most excessive strategy from the other aspects. And the learning strategies that are used to develop spelling skill are the lowest strategy from the other aspects

Symblicit algorithms for optimal strategy synthesis in monotonic Markov decision processes

When treating Markov decision processes (MDPs) with large state spaces, using explicit representations quickly becomes unfeasible. Lately, Wimmer et al. have proposed a so-called symblicit algorithm for the synthesis of optimal strategies in MDPs, in the quantitative setting of expected mean-payoff. This algorithm, based on the strategy iteration algorithm of Howard and Veinott, efficiently combines symbolic and explicit data structures, and uses binary decision diagrams as symbolic representation. The aim of this paper is to show that the new data structure of pseudo-antichains (an extension of antichains) provides another interesting alternative, especially for the class of monotonic MDPs. We design efficient pseudo-antichain based symblicit algorithms (with open source implementations) for two quantitative settings: the expected mean-payoff and the stochastic shortest path. For two practical applications coming from automated planning and LTL synthesis, we report promising experimental results w.r.t. both the run time and the memory consumption.Comment: In Proceedings SYNT 2014, arXiv:1407.493